Machine Learning Predicts Long-Term Risk of Heart Attack

Machine learning tools could predict patients’ long-term risk of heart attacks and cardiac deaths better than standard methods used by cardiologists, according to a study published in Cardiovascular Research. Coronary artery calcium (CAC) scoring with non-contrast computed tomography (CT) is increasingly used for cardiovascular risk stratification and provides a measure of coronary atherosclerotic burden, or the buildup of cholesterol and other substances on the artery walls. The researchers noted that several studies have found that the total CAC measured by non-contrast CT predicts cardiovascular events beyond standard cardiovascular risk factors. However, researchers have not yet used machine learning risk scores after CAC scanning for the prediction of future cardiovascular events.